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Generating Personalized Lower-Limb Kinematics Across Walking Speeds Using Subject-Conditioned Diffusion
Diya Dinesh, Adrian Krieger, Changseob Song +3
Personalizing exoskeleton assistance requires user-specific gait data across many locomotor tasks, yet collecting this data demands repeated motion capture sessions that are costly…
Continual Online Personalization of Exoskeleton Control via Manifold-Aware Experience Replay
Changseob Song, Inseung Kang
Personalizing exoskeleton control remains a critical challenge for clinical users with gait disabilities. Online adaptation (OA) offers an effective solution by adapting in real ti…
Musculoskeletal Motion Imitation for Learning Personalized Exoskeleton Control Policy in Impaired Gait
Itak Choi, Ilseung Park, Eni Halilaj +1
Designing generalizable control policies for lower-limb exoskeletons remains fundamentally constrained by exhaustive data collection or iterative optimization procedures, which lim…
Learning Hip Exoskeleton Control Policy via Predictive Neuromusculoskeletal Simulation
Ilseung Park, Changseob Song, Inseung Kang
Developing exoskeleton controllers that generalize across diverse locomotor conditions typically requires extensive motion-capture data and biomechanical labeling, limiting scalabi…
Optimizing Locomotor Task Sets in Biological Joint Moment Estimation for Hip Exoskeleton Applications
Jimin An, Changseob Song, Eni Halilaj +1
Accurate estimation of a user's biological joint moment from wearable sensor data is vital for improving exoskeleton control during real-world locomotor tasks. However, most state-…
Learning Speed-Adaptive Walking Agent Using Imitation Learning with Physics-Informed Simulation
Yi-Hung Chiu, Ung Hee Lee, Changseob Song +2
Virtual models of human gait, or digital twins, offer a promising solution for studying mobility without the need for labor-intensive data collection. However, challenges such as t…